Effective Demand Management Research Articles

Air pollution reduction co-benefits associated with low-carbon transport initiatives for carbon neutrality in China by 2060

Low-carbon transport policy measures will contribute to greenhouse gas emission reductions, as well as improvements in air quality via air pollutant emission reductions. Here, we developed a regional transport energy model by integrating a transport demand model and an energy system model to explore the associated air pollutant emission reductions achieved by low-carbon transport initiatives when projecting a path towards carbon neutrality. Several scenarios were created to identify the effectiveness and feasibility of low-carbon strategies using the Avoid–Shift–Improve (A-S-I) framework including “Technology”, “Regulation”, “Information”, and “Economy” instruments. Significant reductions in air pollutant emissions alongside transport decarbonization in China could be realized by implementing policy measures to establish effective transport demand management, a modal shift in transport use, and technological improvements. In particular, the “Improve” strategy was more effective in delivering significant reductions in air pollutant emissions than were the “Avoid” and “Shift” strategies, but the policy effects of each A-S-I pillar varied greatly for different pollutants and across different regions. An air pollution reduction strategy for the transport sector should be designed in an integrated manner based on a series of different strategies and tools.

Investigating the influence of Australia Day and Christmas Day on water demand in the Greater Sydney region

ABSTRACT The objective of this research is to investigate whether particular occasions, such as Australia Day and Christmas Day, have a notable impact on water demand in the Greater Sydney region. By examining water demand during these events, the study aims to enhance understanding of water consumption patterns and contribute to the development of effective demand management strategies. Multivariate time series data from several water plants in the Greater Sydney region were analyzed using three methods: correlation heatmaps, t-tests, and descriptive statistics. The findings indicate that neither Australia Day nor Christmas Day has a significant impact on water demand at different water plants in the Greater Sydney region. These results suggest that public holidays may not need to be a critical factor in short-term water demand forecasting models for this area.

Temperature variation and urban electricity consumption in China: Implications for demand management and planning

With the pressing challenges of climate change and rapid urbanization, understanding the impact of temperature variation on electricity consumption in urban areas is crucial for effective demand management and planning. This study investigates the relationship between temperature and electricity usage in Chinese urban centers, shedding light on critical patterns that can inform sustainable energy policies. Using panel data analysis reveals a significant impact of temperature change on household electricity consumption. The findings indicate an optimal temperature range of 18–24 °C and a non-linear association between temperature and power usage. Notably, the rise in summer temperatures has a greater impact on electricity consumption than the decrease in colder winters. The study also highlights variations in the influence of temperature on electricity consumption based on regional economic development and north-south disparities. The study suggests implications for optimizing power supply planning.

Development of an integrated urban modelling framework for examining the impacts of work from home on travel behavior

This paper develops an integrated urban modelling framework (IUMF) to predict how work from home (WFH) decision affects travel behavior. First, it conducts a questionnaire survey among working professionals in Halifax, Canada, to collect data on their socio-demographic characteristics, mode choice, vehicle ownership, and work-arrangement. Bayesian Belief network models are developed using the collected responses to calculate the cumulative probability tables (CPTs) of variables associated with the decision to WFH. Next, the ascertained CPTs are used as input to extend an integrated urban modelling framework (IUMF) that is further utilized to simulate individuals’ work from home choices and travel behavior up to 2025 for Halifax, Canada. Results indicate that around 57% of the workers would like to WFH and 7% wants to relocate closer to workplace. The model forecasts a significant preference for remote work among individuals with offices in the urban core. Results also show that auto mode share is increased to 79% in 2024, whereas transit, walking and biking trips decreased. Average travel distance is higher in the post-pandemic compared to the pre-pandemic, while travel distance of telecommuters is found to be higher than non-telecommuters. Statistically significant differences are observed between telecommuters and non-telecommuters for ‘number of activities’ and ‘distance travelled’ in a day. The outcomes of this study will offer policy makers a better understanding of long-term impacts of WFH on transport and land-use systems and help to develop effective travel demand management strategies.

Exploring the Efficacy of Random Linear Parameter Models for Forecasting Heating Demand in District Heating Networks

The heating and cooling sector accounts for approximately half of global energy consumption, making it a pivotal focus for energy transition efforts. District heating and cooling networks have emerged as efficient, innovative, and dominant technologies in various regions. Accurate forecasting of heating demand within these networks is imperative for effective energy transition and responsive demand management. While existing forecasting techniques primarily concentrate on limited parameters such as ambient temperature and time of day, this paper explores the multifaceted impact of various factors, including ambient temperature, energy prices, number of consumers, building types, and solar radiation, on heating demand. Through a comprehensive study of these determinants, a robust and efficient forecasting model is developed in this research. Specifically, a novel Random Linear Parameter Model (RLPM) is introduced for hourly heating demand forecasting within district heating networks. The model's performance is assessed and compared with existing forecasting models, namely Linear Regression, ARIMA, and RLPM. A notable advantage of the Random Parameter Linear Regression model lies in its capability to recognize unobserved parameters influencing demand and account for unobserved heterogeneity, providing a robust framework for incorporating these factors into the forecasting process. This study aims to underscore the proposed approach's advantages in terms of accuracy and its ability to estimate the determinants of heating load, thereby offering reliable future forecasting. The findings have the potential to significantly contribute to optimizing district heating systems by delivering more nuanced and dependable predictions of heating demand.

Demand forecasting for platelet usage: From univariate time series to multivariable models.

Platelet products are both expensive and have very short shelf lives. As usage rates for platelets are highly variable, the effective management of platelet demand and supply is very important yet challenging. The primary goal of this paper is to present an efficient forecasting model for platelet demand at Canadian Blood Services (CBS). To accomplish this goal, five different demand forecasting methods, ARIMA (Auto Regressive Integrated Moving Average), Prophet, lasso regression (least absolute shrinkage and selection operator), random forest, and LSTM (Long Short-Term Memory) networks are utilized and evaluated via a rolling window method. We use a large clinical dataset for a centralized blood distribution centre for four hospitals in Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily platelet transfusions along with information such as the product specifications, the recipients' characteristics, and the recipients' laboratory test results. This study is the first to utilize different methods from statistical time series models to data-driven regression and machine learning techniques for platelet transfusion using clinical predictors and with different amounts of data. We find that the multivariable approaches have the highest accuracy in general, however, if sufficient data are available, a simpler time series approach appears to be sufficient. We also comment on the approach to choose predictors for the multivariable models.

Coconut shell-derived activated carbon-enhanced water phase change material for cold thermal energy storage.

In building cooling, the demand for cooling surges during specific times, stressing air-conditioner operation, and additional cooling is often wasted during low-demand periods. Water-phase change material (W-PCM)-based thermal energy storage (TES) allows for load shifting and effective management of peak demand by storing cooling energy when the demand is low. This stored energy can be deployed during peak hours, decreasing energy usage and associated CO2 emissions. However, the use of W-PCMs was hindered by phase separation, slow energy transfer, and high supercooling degree (SCD). We synthesized coconut shell (CNS)-produced activated carbon (ACC) to use as a thermal enhancer in W-PCMs for the first time. First, ACC was synthesized from CNS via steam activation. Then, transmission electron microscopy was used to confirm the pore morphology of the CNS-ACC.The synthesis of the W-PCM with various weight percentages (0.1, 0.6, and 1.2) of CNS-ACC was accomplished in two steps. Zeta potential distribution analysis revealed that the W-PCM with CNS-ACC exhibited colloidal stability. Thermal conductivity (TC) and thermogram analyses revealed that a dose of 1.2 wt% CNS-ACC enhanced liquid and solid TC by 9% and 22%, respectively, despite a 6% and 8% decrease in specific heat and latent heat. More specifically, solidification assessment in a spherical enclosure revealed 100% suppression of SCD with 1.2 wt% CNS-ACC. As a result of this and the enhanced TC, the overall solidification process was accelerated, reducing the overall duration by 18.5%. Thus, the combination of CNS-derived ACC and W-PCM for TES in building cooling could reduce energy consumption and associated CO2 emissions.

Optimal management of microgrid energy by considering demand side management plan and maintenance cost with developed particle swarm algorithm

This paper introduces a pioneering approach to power system optimization by integrating microgrids and demand-side management, with a focus on its novel contributions. Emphasizing cost reduction, the study targets effective demand management through load shifting. A unique microgrid energy management system is proposed for peak-shaving, leveraging the cost advantages of lower controllable unit expenses. The system actively involves microgrid units in responsive load management, reducing peak consumption during high-demand periods. Evaluation of various demand-side management techniques, especially peak-time rebate programs, is conducted through presented indices measuring the energy management solution's effectiveness. To address the complexity, a particle swarm-based algorithm is introduced for computational efficiency. Validation of the proposed method and model in diverse scenarios showcases significant cost reduction, demonstrating the practicality and innovation of the integrated microgrid and demand-side management strategy. The study highlights a substantial reduction in peak load through load response program participation. At a 20 % participation rate with a 10-cent incentive, there's a significant decrease in peak load during specific hours. With increasing participation and incentives, peak load reduction intensifies, reaching optimal results at a 40 % participation rate and a 20-cent incentive.

Contribution of ChatGPT and Other Generative Artificial Intelligence (AI) in Renewable and Sustainable Energy

In the dynamic field of renewable and sustainable energy, Artificial Intelligence (AI) integration has emerged as a crucial catalyst for enhancing efficiency, cost reduction, and addressing multifaceted challenges. This work delves into the performance and contributions of ChatGPT, a prominent representative of large AI language models, within the context of renewable and sustainable energy. The research endeavors to explore ChatGPT's efficacy and role across a diverse spectrum of renewable energy aspects, spanning solar energy, wind energy, biomass energy, hydropower, geothermal energy, energy storage, grid integration, energy efficiency, materials science, policy, economics, environmental impact, energy accessibility, and advanced biofuels. The investigation commences with solar energy, where ChatGPT showcases impressive capabilities in data analysis, modeling, and forecasting. It facilitates solar panel placement optimization, energy production prediction, and overall efficiency enhancement. Likewise, in the realm of wind energy, ChatGPT proves invaluable for tasks such as wind turbine control, maintenance scheduling, and wind speed prediction. In biomass energy, ChatGPT aids in feedstock selection, process optimization, and emissions reduction. In the hydropower sector, it supports reservoir management and river flow prediction, thereby optimizing energy output. Geothermal energy benefits from ChatGPT's geological analysis, reservoir modeling, and exploration strategies. Energy storage solutions are indispensable for grid stability, and ChatGPT contributes to the advancement of cutting-edge battery technologies, augmenting energy storage capacity and lifespan. Furthermore, ChatGPT's insights foster the evolution of smart grids, ensuring efficient energy distribution and effective demand management. Additionally, ChatGPT provides invaluable insights into policy and economics, offering recommendations for incentivizing the adoption of renewable energy sources while considering environmental impact and sustainability. It also contributes to the advancement of tidal and wave energy technologies, as well as the development of advanced biofuels. The outcomes of this research paper underscore the potential of AI and ChatGPT in shaping the future of renewable energy, expediting progress toward a sustainable energy ecosystem.

Optimization of Neutrosophic EOQ Model for Effective Demand Management in Uncertain Environment Using Genetic Optimization

Inventory management is characterized by a continuous struggle to lower goods levels and related costs while also providing customers with the goods they need. However, reducing costs while simultaneously striving for ideal inventory levels is difficult, notably in the current situation of high unpredictability of goods demand and lead time. Traditional inventory models are not strong enough to endure changes like goods demand and lead-time demand. As a result, it must be adjusted to achieve results. The oeuvre below presents a new kind of inventory model that deals with uncertainty in the demand for goods and lead time. In this regard, the presented work, the novel Neutrosophic Economic Order Quantity approach is a mechanism to account for the likely imprecision in the model. Specifically, the Neutrosophic set theory is integrated into the EOQ model so that it can handle variations in the demand and lead-time pattern successfully. An objective function is established for obtaining economical order quantities that include demand, lead-time, and other necessary components’ irregularities. The process variables in the model are given the final values using genetic algorithms and simulated annealing. To highlight the impact of the proposed Neutrosophic approach, it is then applied to several realistic examples. This will provide the audience a sense of how effective inventory management may be in high-uncertainty situations. The rapid evolution of organizations necessitates innovative inventory control tactics to meet growing demands

Effects of a price incentive policy on urban rail transit passengers: A case study in Nanjing, China

To alleviate travel congestion at peak periods or on congested routes, some measures for urban rail transit (URT) systems including fare incentive schemes and subsidy policies have been widely adopted. Existing measures can alleviate demand during peak periods or on congested routes to a certain extent, but they do not consider the passengers’ satisfaction with their travel experience. Consequently, this study aims to investigate passengers’ acceptance of a fare incentive policy offering a discounted fare during off-peak periods or on uncongested routes before it is implemented in Nanjing, China. To understand passengers’ acceptance of the policy, this study explores the effects of the policy on passengers’ route choices. Furthermore, the differences among passengers with respect to different travel purposes and travel times in route choices have been analyzed. A revealed preference (RP) survey and a stated preference (SP) survey consisting of 463 samples from URT passengers in Nanjing, China, were conducted and analyzed using a random-parameter multinomial logit (RPMNL) model. Results show that socio-economic variables, travel characteristics, travel cost, departure time, and travel distance significantly affect passengers’ route choices. Furthermore, the route choices of passengers with different travelling times and purposes vary. Commuters are most sensitive to travel cost during off-peak periods, followed by non-commuters who travel during peak periods. Departure time and travel distance most significantly affect the route choices of non-commuters during peak periods and off-peak periods, respectively. The findings of this study could assist transit agencies in designing attractive fare incentive policies for public transit passengers and offer valuable insights into effective demand management strategies. Moreover, the lessons derived from this study may guide the implementation of fare incentive policies in Nanjing, China, and elsewhere.

Assessment of the factors shaping the public’s perception of physical and virtual water: A case study of Korean households

Water demand management is a crucial approach for ensuring sustainable water supply. To ensure an effective water demand management, it is essential to have a good understanding of public perceptions of water use. In this study, we examined the perceptions of water use and virtual water of Daegu inhabitants in South Korea, with a focus on understanding their sociodemographic characteristics, water-saving behaviors, environmental attitudes, and accuracy in estimating water use and virtual water content. The results indicated that the majority of respondents were aware of the need for water conservation but exhibited only moderate engagement in water-saving behaviors. They consistently underestimated their water use across various activities (portable drinking water, shower, toilet flush, laundry and car wash), indicating a lack of understanding regarding the magnitude of water consumption. Similarly, respondents had limited awareness of the virtual water content in different products, with inaccurate perceptions of water-intensive products. Sociodemographic factors such as gender, education level, annual income, and family size significantly influenced perceptions of water use, while environmental attitudes played a significant role in the perceptions of both water use and virtual water content. The study highlights the need for educational initiatives to enhance public awareness and understanding of water use and virtual water, particularly in addressing misconceptions and promoting sustainable water management practices. While the findings are specific to Daegu city, they provide insights that can inform water management strategies in other cities globally.

Does high-speed rail mitigate peak vacation car traffic to tourist city? Evidence from China

Tourist travel contributes greatly to transport problems in attractive tourist cities. To take full advantage of high-speed rail (HSR) for alleviating massive car traffic during the peak vacation period, this paper analyses the travel modes of domestic visitors to Shaoxing before and after the operation of HSR. Scenario-based comparison and a random-coefficients structure Mixed Logit (MXL) model with error components were adopted to analyze the travel mode change and the factors explaining tourists’ travel mode choices. Our findings show that the HSR modal share increased substantially at the expense of express buses, more than cars. Also, HSR was found to be less competitive than cars on toll-free days for medium short travel distances. The MXL model results indicate that HSR was more likely to be used over automobiles by young people, females, and one-destination travellers, for longer travel distances, and with high service frequency to Shaoxing. Besides, online booking services were highly associated with HSR use. Driving was favoured over HSR by higher income level groups, when travelling with family or friends, on toll-free national holidays. Current government policy to waive road tolls during the peak holiday period further induced car traffic to tourist cities. When individual taste was considered, tourists showed a similar preference in their valuation of the travel time variable, while were heterogenous in their preference for low per-distance cost. Our findings suggest that the adjustment of the road-toll policy, pre-booking design for targeted tourists, and measures to reduce the total travel time of HSR should be considered to promote HSR as well as impede the use of cars during peak periods. This study offers empirical evidence of achieving effective travel demand management and reducing car dependence through HSR and complementary measures.

Intelligent Energy Management for Distributed Power Plants and Battery Storage

A smart energy management controller is required for effective source coordination and load demand management. This work proposes a novel instantaneous current reference technique for use in power management of hybrid power systems (HPS), both autonomous and interconnected with the grid. A grid integrated hybrid power system (GI-HPS) includes both the AC grid and additional sources used in industrial and commercial environments. Solar photovoltaic (SPV) panels, wind turbine generators, proton exchange membrane fuel cells, and batteries are all part of the test system. The suggested energy management system (SEMS) manages power from the hybrid power source and the energy storage components to meet the load needs. The recommended SEMS can transit between 12 different modes of operation to fulfil the load demand requirements. The SEMS employs a scaling factor N to accelerate the rate at which the measured current approaches the reference current. The proposed scaling factor significantly improves the SEMS’s dynamic performance since it can quickly respond to the changes in the source and load characteristics. The dependability of an HPS powered by a variety of renewable energy sources can also be improved.

Insights from residents under year‐round irrigation restrictions to improve water conservation impacts

AbstractYear‐round irrigation water restrictions can be effective water demand management tools if people adhere to them. Awareness and understanding of these regulations have important implications for compliance and long‐term water supply goals. This study quantified irrigation restriction awareness‐knowledge and how‐to knowledge using survey data from residents with automated irrigation systems. Only half of those subject to irrigation restrictions were aware of these regulations; these irrigation restriction aware individuals reported varying perceived allowable watering frequency, implying that misunderstandings exist. Education, living in a homeowners association, hiring specific professionals, and information‐seeking preferences distinguished irrigation restriction aware from unaware individuals. Binary logistic regression revealed that when considered together, race, education, income, hiring a professional for fertilizer application, and living in a homeowner association significantly influence awareness‐knowledge. There are important opportunities to improve conservation outcomes by increasing both awareness and accuracy among target audience members.

COVID-19 and Teleworking: Lessons, Current Issues and Future Directions for Transport and Land-Use Planning

Teleworking has been considered to be one of the emanating behaviors from the pandemic that may become long-lasting. Wider adoption of teleworking may fundamentally change urban mobility and spaces across cities. However, knowledge about the potential implications of teleworking on urban transport and land-use systems post-pandemic is limited. Through a comprehensive review of existing teleworking studies, this research identifies gaps in the literature, discusses major issues for exploration and suggests future research directions. It also explores ways to utilize teleworking as an effective travel demand management strategy. Analysis shows that teleworking has the potential to substantially change city landscapes and can assist in reducing traffic congestion, greenhouse gas emissions, and energy use. Priority areas for further research are identified, such as in-home activities, residential location choice, non-work trip patterns, and energy consumption decisions of teleworkers for a clearer understanding of the relationship between teleworking and urban systems. Analysis also reveals several planning and policy challenges surrounding teleworking, including digital divide, urban sprawling, and transformation of city centers, among others. To fully realize the benefits of teleworking, planners need to reconfigure community design principles to promote mixed-use, lively, and vibrant neighborhoods where people can both live and work. At the same time, governments should consider providing incentives to both organizations and employees with an aim to retain teleworking. Results of this paper will be highly beneficial to transport and land-use researchers, planners, and policy makers.

Are Telecommuters’ and Non-Telecommuters’ Daily Time-Use Behaviors Different? An Episode-Level Model for Non-Mandatory Activities

Telecommuters (individuals working from home), and non-telecommuters (individuals working at the workplace) might have significantly different daily time-use patterns. For example, telecommuters might be able to engage in more episodes of non-mandatory activities such as recreation and pick-up/drop-off of children, as many telecommuters have a relatively flexible work arrangement in relation to their work location and schedule. This study aims to explore the non-mandatory activity engagement and duration decisions of these two worker profiles at a disaggregated-episode level. To do so, a multiple discrete continuous extreme value with ordered preferences (MDCEV-OP) model is adopted, using the 2018 travel survey data from the Central Okanagan region of British Columbia, Canada. This model accounts for multiple occurrences of each activity type (i.e., episodes) along with their corresponding durations. In doing so, the model adopts a logical ordering ensuring that the jth episode is not predicted without the occurrence of the ( j–1)th episode of an activity type. The model results reveal that telecommuters are more likely to engage in higher episodes of non-mandatory activities, compared with non-telecommuters. For instance, female teleworkers are found to be more likely to participate in higher episodes of activities associated with household-related responsibilities such as shopping. In contrast, female non-telecommuters are associated with participating in more episodes of personal business-related activities. The findings of this study provide important behavioral insights into the activity-time-use patterns of telecommuters and non-telecommuters, which can be utilized to develop more effective and equitable travel demand management plans, policies, and models.

The role and potential of trusted change agents and freight demand management in mitigating “Panic Buying” shortages

The goal of this research is to help develop effective freight demand management (FDM) programs to reduce the amounts of “panic buying” purchases. Such programs are indispensable because, as explained in the paper, in “panic buying” environments, supply chains are frequently unable to rapidly increase the amounts of critical supplies delivered to points of sales. Thus, reducing the demand is the only option to mitigate shortages. To reach this goal, this research assesses the potential of a novel FDM initiative that relies on Trusted Change Agents (TCAs) to convince individuals to limit their purchases of critical supplies, and outlines a comprehensive FDM program with initiatives spanning the entire disaster management cycle. The research: analyzes survey data to gain insight into the reasons people participate in “panic buying;” establishes that the term “panic buying” is a misnomer—as the instances of true “panic” are extremely rare—and suggests the term “Disaster Related Buying Behaviors” (DRBBs)” to better describe the phenomena; creates a data-driven taxonomy of objectives and motivating reasons for DRBBs; estimates a Structural Equation Model (SEM) to assess the strengths of the interconnections between reasons, themes, and objectives pursued by individuals who enact DRBBs; assesses the impacts of TCA appeals; identifies the initiatives needed, together with the timing and sequencing, to be conducted at the various stages of the disaster management cycle; identifies the stakeholders that ought to be involved in the executions of these initiatives; and puts these elements together to outline a comprehensive FDM program.

Ценовые параметры поставки электроэнергии как базис управления спросом на электропотребление в регионе

Energy cost management (more than 45 % in the structure of total costs) can significantly reduce own energy supply costs and improve operational efficiency of an enterprise. The study examines regional electricity price parameters, affected by retail and wholesale electricity markets, in terms of the possibilities for industrial enterprises to implement price-dependent demand management to reduce energy purchase costs. The article aims to distribute Russian regions according to the prospects for reducing energy purchase costs by managing electricity demand schedules. The following methods were utilised: statistical analysis of hourly average electricity prices per year, month and day in the regional context; mathematical modelling and calculation of authors’ coefficients of average electricity prices and coefficients of daily price volatility for assessing prospects for effective energy demand management in Russian regions; construction of positioning maps for grouping and identifying regions where the implementation of price-dependent demand management is possible. Electricity price parameters of all Russian regions were examined. The paper analysed principles of electricity pricing for domestic industrial enterprises, researched the impact of price volatility on energy purchase costs, assessed the contribution of electricity costs to total energy consumption costs of enterprises. Coefficients of average electricity prices, coefficients of daily price volatility and coefficients of efficiency of price-dependent electricity consumption were applied to study electricity price parameters in the regional context. As a result, the article presented a map of electricity price parameters, where regions are grouped according to the possibility of implementing demand management mechanisms. Additionally, specific practical recommendations on price-dependent demand management for electricity consumption of industrial enterprises were given for each identified regional group.

Impact of telecommuting on Japanese Citizen's travel, activities, and residential locations: Experiences and future expectations under COVID-19 pandemic

In the past few years, telecommuting has diffused on a large scale across many developed countries; however, its popularity in Japan is low due to various factors and cultural differences. The outbreak of COVID-19 has made the adoption of telecommuting indispensable, reaching an unprecedented level in Japan, bringing dramatic changes in the lifestyles and behaviors of citizens. This study investigates how and to what extent this pandemic-induced telecommuting affected employees’ travel, activities, and residence locations and explores their expectations of post-pandemic life. The data were collected through the online questionnaire survey across three major metropolitan areas in Japan that incorporates a comprehensive set of questions regarding telecommuting implementation and feasibility, lifestyles before and during the pandemic, and expected way of life after the pandemic, and the total of 2045 respondents were collected. The analysis has been done based on revealed preference data considering two time periods i.e., before COVID-19, during COVID-19, and for the post-COVID 19 scenario where stated preferences are considered. In the first step, telecommuting behavior, telecommuting preferences and plans, and activity patterns are estimated based on aggregate sample data. Next, by using the stated preference survey conducted, ordered probit model and mixed logit model are applied to identify factors influencing post-pandemic telecommuting frequency and residential location preference, respectively. The analysis revealed two major findings: telecommuting brought significant changes in multiple aspects of people’s lifestyles and activities that might persist after the pandemic; although determinants that govern telecommuting frequency and residential location choice after COVID-19 may differ across metropolitan areas, commuting/telecommuting frequency is the prime factor influencing residential location decisions. The findings of this research can guide transportation and urban planners in establishing effective travel demand management tools and sustainable urban networks for the new-normal era.